Catalan industry runs on what is not on the internet
The industrial base of Barcelona and its surroundings (automotive, chemicals, food, machinery, engineering firms and technical offices) depends on knowledge that does not appear in any search engine: tolerances, process recipes, fixes for breakdowns, quotes won and lost. That is exactly what AI can make useful and, at the same time, what should never leave the company network.
Regulation is pushing in the same direction. The Trade Secrets Act only protects what has been protected with reasonable measures, and NIS2 brings digital supplier control to many factories that had never considered it. An AI installed on your own server is not one more supplier: it is a machine you own.
What it looks like day to day
A quality engineer asks what happened the last time a customer rejected a batch because of burrs and gets the three relevant 8D reports, with the action that worked. The person in charge of quotes starts from a draft that already combines the customer’s requirements with two similar projects. In maintenance, the technician looks up an error code and the assistant shows the manual page and that machine’s history. And every morning an agent checks incoming orders and flags any part number that does not match the ERP.
What we already do in our lab
This is not theory. In our Barcelona lab, two clustered NVIDIA DGX Spark units and several RTX GPUs run open models of up to 320 billion parameters, with contexts of up to a million tokens, and agents that work 24 hours a day. Our private gateway has served around 11.15 billion tokens at zero cost per token. We know how much memory each model needs, what fails and when, and how to switch models without interrupting the service.
How we deploy it
- Assessment. We classify the information (what is secret, what is public, what contains personal data), look at the systems you have and which tasks take up the most engineering hours. You come away with use cases, an architecture and a fixed proposal.
- Pilot of 2 to 4 weeks. Usually technical search over part of the documentation, with a specific team. We measure search time and answer quality.
- Production. Installation on your network (isolated if needed), integrations with your ERP or document management system, agents for the processes that justify them, and maintenance with controlled model updates.
When you don’t need it
If the information you want to search is public or there are no trade secrets involved, a cloud tool with a suitable contract may be cheaper. And if the problem is that the documentation does not exist, it has to be written first: AI organises and finds knowledge, but it does not invent it.